---
title: ChatContextual
---

This will help you get started with Contextual AI's Grounded Language Model [chat models](/oss/langchain/models/).

To learn more about Contextual AI, please visit our [documentation](https://docs.contextual.ai/).

This integration requires the `contextual-client` Python SDK. Learn more about it [here](https://github.com/ContextualAI/contextual-client-python).

## Overview

This integration invokes Contextual AI's Grounded Language Model.

### Integration details

| Class | Package | Local | Serializable | JS support | Downloads | Version |
| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |
| [ChatContextual](https://github.com/ContextualAI//langchain-contextual) | [langchain-contextual](https://pypi.org/project/langchain-contextual/) | ❌ | beta | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-contextual?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-contextual?style=flat-square&label=%20) |

### Model features

| [Tool calling](/oss/langchain/tools) | [Structured output](/oss/langchain/structured-output) | JSON mode | [Image input](/oss/how-to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/oss/langchain/streaming/) | Native async | [Token usage](/oss/how-to/chat_token_usage_tracking/) | [Logprobs](/oss/how-to/logprobs/) |
| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |
| ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |

## Setup

To access Contextual models you'll need to create a Contextual AI account, get an API key, and install the `langchain-contextual` integration package.

### Credentials

Head to [app.contextual.ai](https://app.contextual.ai) to sign up to Contextual and generate an API key. Once you've done this set the CONTEXTUAL_AI_API_KEY environment variable:

```python
import getpass
import os

if not os.getenv("CONTEXTUAL_AI_API_KEY"):
    os.environ["CONTEXTUAL_AI_API_KEY"] = getpass.getpass(
        "Enter your Contextual API key: "
    )
```

If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:

```python
# os.environ["LANGSMITH_TRACING"] = "true"
# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
```

### Installation

The LangChain Contextual integration lives in the `langchain-contextual` package:

```python
%pip install -qU langchain-contextual
```

## Instantiation

Now we can instantiate our model object and generate chat completions.

The chat client can be instantiated with these following additional settings:

| Parameter | Type | Description | Default |
|-----------|------|-------------|---------|
| temperature | Optional[float] | The sampling temperature, which affects the randomness in the response. Note that higher temperature values can reduce groundedness. | 0 |
| top_p | Optional[float] | A parameter for nucleus sampling, an alternative to temperature which also affects the randomness of the response. Note that higher top_p values can reduce groundedness. | 0.9 |
| max_new_tokens | Optional[int] | The maximum number of tokens that the model can generate in the response. Minimum is 1 and maximum is 2048. | 1024 |

```python
from langchain_contextual import ChatContextual

llm = ChatContextual(
    model="v1",  # defaults to `v1`
    api_key="",
    temperature=0,  # defaults to 0
    top_p=0.9,  # defaults to 0.9
    max_new_tokens=1024,  # defaults to 1024
)
```

## Invocation

The Contextual Grounded Language Model accepts additional `kwargs` when calling the `ChatContextual.invoke` method.

These additional inputs are:

| Parameter | Type | Description |
|-----------|------|-------------|
| knowledge | list[str] | Required: A list of strings of knowledge sources the grounded language model can use when generating a response. |
| system_prompt | Optional[str] | Optional: Instructions the model should follow when generating responses. Note that we do not guarantee that the model follows these instructions exactly. |
| avoid_commentary | Optional[bool] | Optional (Defaults to `False`): Flag to indicate whether the model should avoid providing additional commentary in responses. Commentary is conversational in nature and does not contain verifiable claims; therefore, commentary is not strictly grounded in available context. However, commentary may provide useful context which improves the helpfulness of responses. |

```python
# include a system prompt (optional)
system_prompt = "You are a helpful assistant that uses all of the provided knowledge to answer the user's query to the best of your ability."

# provide your own knowledge from your knowledge-base here in an array of string
knowledge = [
    "There are 2 types of dogs in the world: good dogs and best dogs.",
    "There are 2 types of cats in the world: good cats and best cats.",
]

# create your message
messages = [
    ("human", "What type of cats are there in the world and what are the types?"),
]

# invoke the GLM by providing the knowledge strings, optional system prompt
# if you want to turn off the GLM's commentary, pass True to the `avoid_commentary` argument
ai_msg = llm.invoke(
    messages, knowledge=knowledge, system_prompt=system_prompt, avoid_commentary=True
)

print(ai_msg.content)
```

## Chaining

We can chain the Contextual Model with output parsers.

```python
from langchain_core.output_parsers import StrOutputParser

chain = llm | StrOutputParser

chain.invoke(
    messages, knowledge=knowledge, systemp_prompt=system_prompt, avoid_commentary=True
)
```

## API reference

For detailed documentation of all ChatContextual features and configurations head to the Github page: [github.com/ContextualAI//langchain-contextual](https://github.com/ContextualAI//langchain-contextual)
